{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Variable transformers : ReciprocalTransformer\n",
    "\n",
    "The ReciprocalTransformer() applies the reciprocal transformation 1 / x\n",
    "to numerical variables.\n",
    "\n",
    "The ReciprocalTransformer() only works with numerical variables with non-zero\n",
    "values. If a variable contains the value  the transformer will raise an error.\n",
    "\n",
    "**For this demonstration, we use the Ames House Prices dataset produced by Professor Dean De Cock:**\n",
    "\n",
    "Dean De Cock (2011) Ames, Iowa: Alternative to the Boston Housing\n",
    "Data as an End of Semester Regression Project, Journal of Statistics Education, Vol.19, No. 3\n",
    "\n",
    "http://jse.amstat.org/v19n3/decock.pdf\n",
    "\n",
    "https://www.tandfonline.com/doi/abs/10.1080/10691898.2011.11889627\n",
    "\n",
    "The version of the dataset used in this notebook can be obtained from [Kaggle](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from feature_engine.imputation import ArbitraryNumberImputer\n",
    "from feature_engine.transformation import ReciprocalTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
       "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
       "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
       "3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n",
       "\n",
       "  YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0   2008        WD         Normal     208500  \n",
       "1   2007        WD         Normal     181500  \n",
       "2   2008        WD         Normal     223500  \n",
       "3   2006        WD        Abnorml     140000  \n",
       "4   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load data\n",
    "\n",
    "data = pd.read_csv('houseprice.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1022, 79), (438, 79))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's separate into training and testing set\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.drop(['Id', 'SalePrice'], axis=1), data['SalePrice'], test_size=0.3, random_state=0)\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ReciprocalTransformer(variables=['LotArea', 'GrLivArea'])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transform 2 variables\n",
    "\n",
    "rt = ReciprocalTransformer(variables = ['LotArea', 'GrLivArea'])\n",
    "\n",
    "rt.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LotArea', 'GrLivArea']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# variables to transform\n",
    "\n",
    "rt.variables_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transforming variables\n",
    "train_t = rt.transform(X_train)\n",
    "test_t = rt.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'GrLivArea')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# variable before transformation\n",
    "X_train['GrLivArea'].hist(bins=50)\n",
    "plt.title('Variable before transformation')\n",
    "plt.xlabel('GrLivArea')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'GrLivArea')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# transformed variable\n",
    "train_t['GrLivArea'].hist(bins=50)\n",
    "plt.title('Transformed variable')\n",
    "plt.xlabel('GrLivArea')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'LotArea')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# tvariable before transformation\n",
    "X_train['LotArea'].hist(bins=50)\n",
    "plt.title('Variable before transformation')\n",
    "plt.xlabel('LotArea')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'LotArea')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# transformed variable\n",
    "train_t['LotArea'].hist(bins=50)\n",
    "plt.title('Variable before transformation')\n",
    "plt.xlabel('LotArea')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# return variables to original representation\n",
    "\n",
    "train_orig = rt.inverse_transform(train_t)\n",
    "test_orig = rt.inverse_transform(test_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# inverse transformed variable distribution\n",
    "\n",
    "train_orig['LotArea'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# inverse transformed variable distribution\n",
    "\n",
    "train_orig['GrLivArea'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Automatically select numerical variables\n",
    "\n",
    "We cannot do reciprocal transformation when the variable values are zero so we will use only positive variables for this demo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load numerical variables only\n",
    "\n",
    "variables = ['LotFrontage', 'LotArea',\n",
    "             '1stFlrSF', 'GrLivArea',\n",
    "             'TotRmsAbvGrd', 'SalePrice']\n",
    "\n",
    "data = pd.read_csv('houseprice.csv', usecols=variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1022, 5), (438, 5))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's separate into training and testing set\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.drop(['SalePrice'], axis=1), data['SalePrice'], test_size=0.3, random_state=0)\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impute missing values\n",
    "\n",
    "arbitrary_imputer = ArbitraryNumberImputer(arbitrary_number=2)\n",
    "\n",
    "arbitrary_imputer.fit(X_train)\n",
    "\n",
    "# impute variables\n",
    "train_t = arbitrary_imputer.transform(X_train)\n",
    "test_t = arbitrary_imputer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ReciprocalTransformer()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reciprocal transformation\n",
    "\n",
    "rt = ReciprocalTransformer()\n",
    "\n",
    "rt.fit(train_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LotFrontage', 'LotArea', '1stFlrSF', 'GrLivArea', 'TotRmsAbvGrd']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# variables to transform\n",
    "rt.variables_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AD7VPLvcB17ajMTYD5wHfXpGdGIDM/ExmbsrMCTpfSj6Umdcx4nVZkmF/i1rxBnwVOAT8jM582w105uIeBJ4B/gk4o20bdH744llgHzA553V+m84XLgeATwx7v/pQl1+jMz3yXeDxdvvwqNcG+GXgsVaXJ4A/b+3voBM0B4CvAae09lPb+oH2+DvmvNaftXo9DXxo2PvWxxpN8eZRKNalx5un0ktSUU6hSFJRBrgkFWWAS1JRBrgkFWWAS1JRBrgkFWWAS1JR/w+dfRazql6e0gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# before transforming \n",
    "\n",
    "train_t['GrLivArea'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VrzfIzOPt/gTwFXq/dfPpiNgM0O5PtOFdtZmvfesc7WvJStSi6zFGWmY+nZm/yMxfAv9A77UDw9fmR8CmiNgwq/0F+2r957TxIyUiXkov2D+fmV9uzaVfO2sx3NfNrzeIiLMj4uWnloG3Aw/Re76nPqnfRe8cIq39+vZp/3bgZPsv4deBt0fEue2/5m+nd870KeAnEbG9fbp/fd++1oqVqEXXY4y0U6HS/D691w70ns+17UqXC4AL6X0gOOd7qx1x3gtc07afXedTtbkGuKeNHxnt3/MW4NHM/HhfV+3Xzmp/uHGaH4jspPeJ9xPAR1Z7Psv4PF9F74qF7wMPn3qu9M5pHgIeB/4VOK+1B70/lPIE8CAw0bevPwCm2+29fe0T9N70TwB/zwh/GAZ8gd7phf+ld15z90rUousxRunWUZvPtef+AL2Q2dw3/iPteT5G3xVSXe+t9lr8TqvZPwNntfaXtfXp1v+q1a7FHLV5M73TIQ8A97fbzuqvHX/9gCQVtBZPy0iSBjDcJakgw12SCjLcJakgw12SCjLcJakgw12SCvo/fFRZkspaUf0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# before transforming \n",
    "train_t['LotArea'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform variables\n",
    "train_t = rt.transform(train_t)\n",
    "test_t = rt.transform(test_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# transformed variable\n",
    "train_t['GrLivArea'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# transformed variable\n",
    "train_t['LotArea'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# return variables to original representation\n",
    "\n",
    "train_orig = rt.inverse_transform(train_t)\n",
    "test_orig = rt.inverse_transform(test_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# inverse transformed variable distribution\n",
    "\n",
    "train_orig['LotArea'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AD7VPLvcB17ajMTYD5wHfXpGdGIDM/ExmbsrMCTpfSj6Umdcx4nVZkmF/i1rxBnwVOAT8jM582w105uIeBJ4B/gk4o20bdH744llgHzA553V+m84XLgeATwx7v/pQl1+jMz3yXeDxdvvwqNcG+GXgsVaXJ4A/b+3voBM0B4CvAae09lPb+oH2+DvmvNaftXo9DXxo2PvWxxpN8eZRKNalx5un0ktSUU6hSFJRBrgkFWWAS1JRBrgkFWWAS1JRBrgkFWWAS1JR/w+dfRazql6e0gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# inverse transformed variable distribution\n",
    "\n",
    "train_orig['GrLivArea'].hist(bins=50)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fenotebook",
   "language": "python",
   "name": "fenotebook"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
